## Documentation Center |

The `intlinprog` function
solves mixed-integer linear programming problems. For more information,
see the reference page and the documentation.

There are several new featured examples of mixed-integer linear programming:

Currently, you cannot run `intlinprog` in
the Optimization
app.

When you do not specify an algorithm, `fmincon` and `quadprog` default to different algorithms
than before. Usually, the new algorithms are faster and more robust
than the algorithms they replace.

Solver | Previous Default Algorithm | New Default Algorithm |
---|---|---|

fmincon | 'trust-region-reflective' | 'interior-point' |

quadprog | 'trust-region-reflective' | 'interior-point-convex' |

Solvers can produce different results than before. To reproduce
previous results, set the `Algorithm` option with `optimoptions`.

options = optimoptions('fmincon','Algorithm','trust-region-reflective'); % or options = optimoptions('quadprog','Algorithm','trust-region-reflective');

Be sure to pass `options` in your function
call.

x = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nlcon,options) % or x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0,options)

The `lsqcurvefit` and `lsqnonlin` solvers have slightly different
behavior than before when the internally calculated trust-region radius
gets large in the `'trust-region-reflective'` algorithm,
or the Levenberg-Marquardt parameter gets small in the `'levenberg-marquardt'` algorithm.
There are no longer any arbitrary limits on these parameters. `fsolve` has the same change to its `'trust-region-reflective'` and `'levenberg-marquardt'` algorithms.

The `UseParallel` option now accepts the values `true` and `false`.
The option also accepts the former values `'always'` and `'never'`,
and scalar values `1` and `0`.

The affected solvers are `fmincon`, `fgoalattain`,
and `fminimax`.

The `ktrlink` function uses
a different default math library within KNITRO^{®} than before. This
change can enable `ktrlink` to run on more hardware
versions.

With the new default math library, `ktrlink` runs
slower than before. To use the previous math library, set the KNITRO format
option `blasoption` to 1. See Setting Options.

`intlinprog` solves more
problems than `bintprog`, and has
better performance. So `bintprog` will be removed
in a future release.

To update your existing `bintprog` code to
use `intlinprog`, see Tips.

`ktrlink` will be removed
in a future release. For an updated KNITRO interface, contact
Ziena Optimization: `www.ziena.com`.

All nonlinear solvers now check whether derivatives of the objective
and nonlinear constraint functions are well defined at the initial
point, usually called `x0`. (This is in addition
to the existing checks that the functions are well defined at `x0`.)
Well defined means the value of each derivative is not `NaN`, `Inf`,
or complex (nonlinear least-squares solvers allow complex values).
Derivatives include both gradients and Jacobians. See Including
Derivatives and Writing
Vector and Matrix Objective Functions.

When the objective or nonlinear constraint functions do not
include a derivative, solvers approximate derivatives by finite differences.
This means that the functions must be well defined for points in a
small neighborhood of `x0`.

If any derivative is not well defined at `x0`,
the solver stops with an error, and does not attempt to find a solution.

In all tested cases, this behavior led to a clearer exit condition.

It is conceivable that a problem that previously ran to completion will now exit without completion. This can occur in the rare case when a derivative did not exist for a function at the initial point, but the solver was able to step to a point where the derivatives were well defined.

The new `optimoptions` function
creates and modifies options for all solvers except the base MATLAB^{®} solvers `fminbnd`, `fminsearch`, `fzero`,
and `lsqnonneg`. Continue to use `optimset` for
those functions.

`optimoptions` organizes options by solver,
with a more focused and comprehensive display than `optimset`:

Creates and modifies only the options that apply to a solver

Shows your option choices and default values for a specific solver/algorithm

Displays links for more information on solver options and other available solver algorithms

For details, see the `optimoptions` page,
or Set Options.

If you export options or a problem from the Optimization Tool,
the options are an object as created by `optimoptions`.
Therefore, earlier software versions cannot import the options or
problem. This consideration does not apply to the base MATLAB solvers,
which continue to encapsulate options as a structure.

Use the `Algorithm` option to select the algorithm
in all solvers that have multiple algorithms. In a future release,
solvers will no longer use the `LargeScale` option.
The `linprog` `Simplex` option
will also change to the `Algorithm` option.

Update options as follows.

Solver | Previous Setting | Current Setting |
---|---|---|

fminunc | 'LargeScale' = 'on' | 'Algorithm' = 'trust-region' |

'LargeScale' = 'off' | 'Algorithm' = 'quasi-newton' | |

linprog | 'LargeScale' = 'on' | 'Algorithm' = 'interior-point' |

'LargeScale' = 'off', 'Simplex'
= 'off' or unset | 'Algorithm' = 'active-set' | |

'LargeScale' = 'off', 'Simplex'
= 'on' | 'Algorithm' = 'simplex' | |

lsqlin | 'LargeScale' = 'on' | 'Algorithm' = 'trust-region-reflective' |

'LargeScale' = 'off' | 'Algorithm' = 'active-set' |

The `ktrlink` function now supports KNITRO version
8.1. For details, see ktrlink:
An Interface to KNITRO Libraries.

The default

`fmincon`algorithm will become`'interior-point'`.The default

`quadprog`algorithm will become`'interior-point-convex'`.

These solvers now warn when you run them in cases where the default behavior will change. For example, they warn when:

You do not set the

`Algorithm`option.You set incompatible

`LargeScale`and`Algorithm`options.

To avoid these warnings:

Do not set the

`LargeScale`option.Set the

`Algorithm`option appropriately.

For details, see the `fmincon` and `quadprog` function
reference pages or Choosing
the Algorithm.

The `ktrlink` function
now supports KNITRO version 8. For details, see ktrlink:
An Interface to KNITRO Libraries.

The `fminunc` medium-scale
algorithm now attempts to recover from failures when evaluating the
objective function during iteration steps, or during gradient estimation.
Failure means the objective function returns `NaN`,
a complex value, or `Inf`. If there is such a failure,
the algorithm attempts to take different steps.

As part of robustness, the `fminunc` medium-scale
algorithm now uses the `ObjectiveLimit` tolerance.

When objective function values drop below `ObjectiveLimit` (default
value: `-1e20`), iterations end with a `-3` exit
flag. Use `optimset` to
change the value of `ObjectiveLimit`. Set `ObjectiveLimit` to `-Inf` to
disable this tolerance.

The `FinDiffRelStep` option for choosing relative
finite difference step sizes is now available in the Optimization
Tool, in the **Approximated derivatives** pane. This
option lets you tune the gradient estimation step in most solvers.

The `fsolve`, `lsqcurvefit`,
and `lsqnonlin` solvers
no longer use the magnitude of the Levenberg-Marquardt regularization
parameter as a stopping criterion, so they no longer return an exit
flag of `-3` when using the `levenberg-marquardt` algorithm.
Instead, they use the `TolX` tolerance in all internal
calculations.

The solvers now stop with exit flag `2` in
most situations where previously they stopped with exit flag `-3`.

The `fmincon` `sqp` algorithm
calculates its Lagrange multiplier estimates somewhat differently
than before.

The `fmincon` `sqp` algorithm
can give slightly different results than before.

The

`fsolve`,`lsqcurvefit`, and`lsqnonlin`solvers now accept the`FinDiffType`option. Set`FinDiffType`to`'central'`with`optimset`to enable derivative estimation by central finite differences. Central finite differences are more accurate, but take more time than the default`'forward'`finite differences.`fsolve`,`lsqcurvefit`, and`lsqnonlin`now use the`TypicalX`option when estimating dense Jacobians via finite differences. In previous releases, these solvers used`TypicalX`only when checking derivatives.For algorithms that obey bounds, finite difference steps for derivative estimation now stay within any bounds you set for the decision variables. See Iterations Can Violate Constraints.

The new

`FinDiffRelStep`option allows you to set a vector of finite difference step sizes to better handle problems whose components have different scales. Use`FinDiffRelStep`at the command line for any solver that uses finite differences. For details, see`FinDiffRelStep`in Options Structure.

The `fsolve`, `lsqcurvefit`,
and `lsqnonlin` functions
no longer use the Gauss-Newton algorithm.

The previous way of selecting the Gauss-Newton algorithm was
to set the `LargeScale` option to `'off'`,
and in:

`fsolve`— set the`NonlEqnAlgorithm`option to`'gn'`.`lsqcurvefit`or`lsqnonlin`— set the`LevenbergMarquardt`option to`'off'`.

To select an algorithm, use `optimset` to
set the `Algorithm` option:

`fsolve`—`trust-region-dogleg`,`trust-region-reflective`, or`levenberg-marquardt``lsqcurvefit`or`lsqnonlin`—`trust-region-reflective`or`levenberg-marquardt`

Solvers no longer use the `LevenbergMarquardt`, `LineSearchType`,
and `NonlEqnAlgorithm` options, since these options
relate only to the Gauss-Newton algorithm.

The `DerivativeCheck` option checks whether
a solver's finite-difference approximations match the gradient
or Jacobian functions that you supply. When a solver finds a discrepancy
between the computed derivatives and their finite-difference approximations,
the solver now errors. Solvers used to pause in this situation instead
of erroring.

Additionally, solvers now compare derivatives at a point near
the initial point `x0`, but not exactly at `x0`.
Previously, solvers performed the comparison at `x0`.
This change usually gives more reliable `DerivativeCheck` decisions.
For details, see Checking
Validity of Gradients or Jacobians.

Solvers do not include the computations for `DerivativeCheck` in
the function count. See Iterations
and Function Counts.

Solvers compare the derivatives at a different point than before, so can change their decision on whether the derivatives match. Solvers now error instead of pause when they encounter a discrepancy.

The `fmincon` `interior-point` and `sqp` algorithms
can use the `ScaleProblem` option. The default value
of `ScaleProblem` is now `'none'` instead
of `'obj-and-constr'`.

Because of a bug in previous releases, when you did not provide
gradients of the objective and nonlinear constraint functions, `fmincon` did
not scale these functions. `fmincon` did scale
linear constraints. So, if you do not provide gradients and have no
linear constraints, the current `fmincon` behavior
is the same as in previous releases. However, the current behavior
can differ if you do provide gradients (`GradObj` or `GradConstr` is `'on'`).
If you provide gradients, have no linear constraints, and want to
obtain the previous behavior, set `ScaleProblem` to `'obj-and-constr'` with `optimset`.

The `fsolve` `trust-region-dogleg` algorithm
no longer performs an internal calculation of conditioning. This change
usually speeds `fsolve`.

`fsolve` iterations
differ from previous versions. Additionally, the solution and all
associated outputs can differ from previous versions. Usually, results
are numerically equivalent to previous results.

For R2011b, error and warning message identifiers have changed in Optimization Toolbox™.

If you have scripts or functions that use message identifiers
that changed, you must update the code to use the new identifiers.
Typically, message identifiers are used to turn off specific warning
messages, or in code that uses a `try`/`catch` statement
and performs an action based on a specific error identifier.

For example, the `'optim:fmincon:ConstrainedProblemsOnly'` identifier
has changed to `'optimlib:fmincon:ConstrainedProblemsOnly'`.
If your code checks for `'optim:fmincon:ConstrainedProblemsOnly'`,
you must update it to check for `'optimlib:fmincon:ConstrainedProblemsOnly'` instead.

To determine the identifier for a warning, run the following command just after you see the warning:

[MSG,MSGID] = lastwarn;

This command saves the message identifier to the variable `MSGID`.

To determine the identifier for an error, run the following command just after you see the error:

exception = MException.last; MSGID = exception.identifier;

`quadprog` has
a new algorithm named `'interior-point-convex'`.
It has these features:

The algorithm has fast internal linear algebra.

The algorithm handles sparse problems.

There is a new presolve module that can improve speed, numerical stability, and detection of infeasibility.

The algorithm handles large convex problems, and accepts and uses sparse inputs. See Large-Scale vs. Medium-Scale Algorithms.

The algorithm optionally gives iterative display.

The algorithm has enhanced exit messages.

For details on the algorithm, see interior-point-convex quadprog Algorithm. For help choosing the algorithm to use, see Quadratic Programming Algorithms.

You now choose the `quadprog` algorithm by
using `optimset` to
set the `Algorithm` option instead of the `LargeScale` option.
If you don't set `Algorithm` or `LargeScale`, `quadprog` behaves
as before.

`Algorithm` option choices are:

`trust-region-reflective`(formerly`LargeScale = 'on'`), the default`active-set`(formerly`LargeScale = 'off'`)`interior-point-convex`

The previous way of choosing the `quadprog` algorithm
at the command line was to set the `LargeScale` option
to `'on'` or `'off'`. `quadprog` now
ignores the `LargeScale` option, except when you
set the inconsistent values `LargeScale` = `'off'` and `Algorithm` = `'trust-region-reflective'`.
In this case, to avoid backward incompatibility, `quadprog` honors
the `LargeScale` option, and uses the `'active-set'` algorithm.

`quadprog` now checks whether any inputs
are complex, and, if so, it errors. The only exception is the `Hinfo` argument
for the `HessMult` option is allowed to be complex.

More solvers now attempt to recover from errors in the evaluation
of objective functions and nonlinear constraint functions during iteration
steps, or, for some algorithms, during gradient estimation. The errors
include results that are `NaN` or `Inf` for
all solvers, or complex for `fmincon` and `fminunc`.
If there is such an error, the algorithms attempt to take different
steps. The following solvers are enhanced:

`fmincon``trust-region-reflective`algorithm (the`interior-point`and`sqp`algorithms already had this robustness)`fminunc``LargeScale`algorithm`fsolve``trust-region-reflective`,`trust-region-dogleg`, and`levenberg-marquardt`algorithms`lsqcurvefit``trust-region-reflective`and`levenberg-marquardt`algorithms`lsqnonlin``trust-region-reflective`and`levenberg-marquardt`algorithms

The `DiffMinChange` and `DiffMaxChange` options
set the minimum and maximum possible step sizes for finite differences
in gradient estimation. The defaults are now:

`DiffMinChange = 0`(formerly`1e-8`)`DiffMaxChange = Inf`(formerly`0.1`)

Solvers have mechanisms that ensure nonzero and non-infinite step sizes, so the new defaults simply mean that the step size adjustment algorithms have fewer constraints.

The new defaults remove the previous arbitrary choices. The previous values can be inappropriate when components are too large or small in magnitude. Tests show these new defaults are good for most situations.

Some solver iterations can differ from previous ones. To obtain the previous behavior:

options = optimset('DiffMinChange',1e-8,'DiffMaxChange',0.1);

For the `trust-region-reflective` algorithm,
the `algorithm` field of the `output` structure
is now `'trust-region-reflective'`. This value differs
slightly from the previous values returned by `fmincon`, `fsolve`, `lsqcurvefit`, `lsqnonlin`,
and `quadprog`.

To avoid errors or unexpected results, update any code that
depends on the exact value of the `output.algorithm` string.

`ktrlink` is
compatible with KNITRO 7.
For details, see ktrlink: An
Interface to KNITRO Libraries, or the Ziena Optimization web
site `http://www.ziena.com/`.

The `fmincon` `interior-point` and `sqp` algorithms
now attempt to recover from errors in the evaluation of objective
functions and nonlinear constraint functions during gradient estimation.
The errors include results that are `NaN`, `Inf`,
or complex. If there is such an error, the finite differencing routines
attempt to take different steps.

The `ktrlink` function
now works with Macintosh 64-bit systems. Therefore, `ktrlink` works
on the same systems as all other Optimization Toolbox functions.

All `linprog` and `quadprog` algorithms
now create a `firstorderopt` field in the output
structure. This field contains the value of the first-order
optimality measure at the final point.

All `fmincon` and `quadprog` algorithms
now create a `constrviolation` field in the output
structure. This field contains the largest value of the constraint
functions at the final point: bounds, linear constraints, and nonlinear
constraints. (Some algorithms return the larger of the constraint
functions and 0.) See Writing Constraints.

There is a new two-part demo on modeling and solving optimization problems. View the first part from the MATLAB command line by entering

playbackdemo('Optimization-Modeling-1','toolbox/optim/web/demos');playbackdemo('Optimization-Modeling-1','toolbox/optim/web/demos');

View the second part by entering

`fmincon` has
a new algorithm called `SQP` for Sequential Quadratic
Programming. The algorithm has the following features:

Honors bounds at all iterations

Attempts a different step if one leads to an objective or constraint function returning a

`NaN`,`Inf`, or complex resultFast internal linear algebra for solving quadratic programs

Choose the algorithm at the command line by setting the `Algorithm` option
to `'sqp'` with `optimset`.
For more information about the algorithm, see fmincon
SQP Algorithm in the Optimization Toolbox documentation.

The `lsqnonneg` solver
no longer accepts a start point `x0` as an optional
input.

The Optimization Tool no longer has an input region for accepting
a start point. If you import or run a problem that contains a start
point `x0`, MATLAB issues a warning. Also, the
Optimization Tool and `lsqnonneg` ignore `x0`,
and instead use a start point of a vector of zeroes. If you export
a problem structure from the Optimization Tool, there is no `x0` field.

Enhanced, clearer exit messages in `fsolve`, `lsqnonlin`,
and `lsqcurvefit`, with links for more information.
For more information about the enhancements, see Exit Flags and
Exit Messages.

For solvers with enhanced exit messages, the content of `output.message` contains
many more characters than before. User code that relies on this field
might need to be modified in order to display the larger exit message
satisfactorily.

The `fmincon` interior-point algorithm attempts
to continue when a user-supplied objective or constraint function
returns `Inf`, `NaN`, or a complex
result. For more information, see fmincon
Interior Point Algorithm.

The large-scale `quadprog` algorithm now
uses the `TolFun` and `MaxIter` tolerances
for deciding when to end iterations when there are only linear equality
constraints, instead of the `TolPCG` and `MaxPCGIter` tolerances.

The `quadprog` `output` structure
now contains the `constrviolation` field, which reports
the maximum constraint function at the final point.

For large-scale linear equality constrained problems, the default
values of the tolerances are much tighter than before, so `quadprog` can
take more iterations, but the resulting solution should be more accurate.

The large-scale interior-point algorithm of `linprog` now
has a backtracking mechanism for the case of stalling, and performs
LDL factorization when there is rank deficiency. For more information,
see Large
Scale Linear Programming.

The `linprog` `output` structure
now contains the `constrviolation` field, which reports
the maximum constraint function at the final point.

The interior-point algorithm of `linprog` might
arrive at different solutions than before, and can solve more problems
than before.

The `optimValues` structure, used by output
functions, has two new fields to better reflect the state of multiobjective
solvers:

For

`fgoalattain`, the`optimValues.attainfactor`field contains the value of*γ*, the attainment factor.For

`fminimax`, the`optimValues.maxfval`field contains the value max_{i}*F*_{i}, where*F*is the vector of objectives.

Furthermore, the value stored in `optimValues.fval` has
changed. Now `optimValues.fval` contains the vector *F* of
objective function values. For a complete description of the current `optimValues` structure,
see Fields
in optimValues.

User code that uses the `optimValues.fval` field
within an output function in `fgoalattain` and `fminimax` might
need to be updated to avoid errors

The `fmincon` solver's `interior-point` algorithm
can now compute finite differences in parallel in order to speed the
estimation of gradients. For details on how to use this parallel gradient
estimation, see the Parallel Computing
for Optimization chapter in the User's Guide.

Solvers print exit messages by default at the end of their runs. The exit messages are different in R2009a for several solvers, and the messages have been enhanced with new functionality. The following sections describe the new features and changes. There is more information in the Exit Flags and Exit Messages section of the User's Guide.

The following solvers have enhanced exit messages:

`fgoalattain``fmincon``fminimax``fminunc``fseminf`

The enhanced exit messages include hyperlinks within their exit messages. These hyperlinks bring up a window containing further information about the terms used in the exit messages.

A `<stopping criteria details>` hyperlink
may appear at the end of an exit message, depending on the solver
and setting of the `Display` option. This link causes
the solver to print more detail about the exit conditions to the MATLAB Command
Window.

There are new values of the `Display` option
to control whether detailed exit messages appear instead of the default
(simpler) messages. The new values are:

`'final-detailed'``'iter-detailed'``'notify-detailed'`

These settings have the same effect as the corresponding settings
without `'-detailed'`, but give detailed exit messages
instead of the default exit messages. For solvers without the new
exit messages, the `'-detailed'` options give the
same behavior as without `'-detailed'`.

For solvers with enhanced exit messages, the `message` field
of the output structure contains both the default (simpler) and the
detailed exit messages, separated by a line of text stating `Stopping
criteria details:`. The message field does not contain hyperlinks;
it contains only text.

For solvers with enhanced exit messages, the content of `output.message` contains
many more characters than before. User code that relies on this field
may need to be modified in order to display the larger exit message
satisfactorily.

The simplex algorithm of `linprog` now detects
when there is no progress in the solution process. It attempts to
continue by performing bound perturbation.

The simplex algorithm of `linprog` might
arrive at different solutions than before, and can solve more problems
than before.

One exit flag in the `fminunc` medium-scale
solver was changed from –2 to 5. This flag appears when the
solver predicts a change in function value at the next step in its
iterations will be less than the `TolFun` tolerance.
This condition can occur at a relative minimum, which should be reported
by a positive flag.

This change might cause users (or code) that examine exit flags to evaluate a result more favorably than previously, since positive exit flags represent normal termination of solvers.

There are two new demos:

A demo showing how to use Symbolic Math Toolbox™ functions to help calculate gradients and Hessians. Run the demo at the MATLAB command line by entering

`echodemo symbolic_optim_demo`.A demo showing how to use

`fseminf`for investigating the effect of parameter uncertainty. Run the demo at the MATLAB command line by entering`echodemo airpollution`.

Furthermore, the optimization tutorial demo now shows how to
include extra parameters. Run the demo at the MATLAB command
line by entering `echodemo tutdemo`.

The Levenberg-Marquardt algorithm was refactored in the solvers

`fsolve`,`lsqcurvefit`and`lsqnonlin`. It is now a more standard implementation, that accepts and preserves sparse Jacobians.Choose between the algorithms used in

`fsolve`,`lsqcurvefit`and`lsqnonlin`using the new`Algorithm`option.There is a new

`ScaleProblem`option that can sometimes help the Levenberg-Marquardt algorithm converge.The default

`fsolve`algorithm,`'trust-region-dogleg'`, has been validated to work with sparse Jacobians.

The refactored Levenberg-Marquardt algorithm can cause

`fsolve`,`lsqcurvefit`and`lsqnonlin`to yield different answers than before.The previous way of choosing the algorithm at the command line was to set the

`LargeScale`option to`'on'`or`'off'`, and, for all solvers but`fsolve`, to set the`LevenbergMarquardt`option to`'on'`or`'off'`. For`fsolve`, in addition to the`LargeScale`option, you needed to set the`NonlEqnAlgorithm`option appropriately.`LargeScale`,`NonlEqnAlgorithm`, and`LevenbergMarquardt`are now ignored, except when choosing to use the Gauss-Newton algorithm.The Gauss-Newton algorithm warns that soon it may no longer be available.

The default value of the

`MaxFunEvals`option in the refactored Levenberg-Marquardt algorithm is now 200*`numberOfVariables`; the previous value was 100*`numberOfVariables`.

You can now access built-in parallel functionality in Optimization Tool for relevant Optimization Toolbox solvers and, if licensed, Global Optimization Toolbox solvers. The option is available when you have a license for Parallel Computing Toolbox™ functions.

The following solvers can now use central finite differences for gradient estimation:

The `fmincon` active-set algorithm and `fminunc` medium-scale
algorithm gained central finite differences this release. The `fmincon` interior-point
algorithm already had them, and the trust-region-reflective algorithm
for both solvers requires a user-supplied gradient, so does not use
finite differences.

To use central finite differences, use `optimset` to
set the `FinDiffType` option to `'central'` instead
of the default `'forward'`. This causes the solver
to estimate gradients by formulae such as

instead of

Central finite differences take twice as many function evaluations as forward finite differences, but are usually much more accurate.

Central finite differences can work in parallel for gradient
estimation in `fgoalattain`, `fmincon` active-set
algorithm, and `fminimax`.
For details on how to use this parallel gradient estimation, see the Parallel Computing
for Optimization chapter in the User's Guide.

`lsqnonneg` was
refactored. It can now use sparse matrices, and it preserves sparsity
during its execution.

A subroutine for gradient estimation by forward finite differences
in nonlinear solvers had a bug that affected it when the current point `x` had
a component with the value 0. Forward finite differences are typically
calculated with a step size proportional to `sqrt(eps)`,
which is about 1.5*10^{–8}. When a
component of `x` was 0, the step size would instead
be proportional to `DiffMinChange`, which has a default
value of 10^{–8}. There is now no difference
in step size when `x` is 0.

Nonlinear solvers can run slightly differently whenever an iteration
causes a component of `x` to be zero, and gradients
are estimated by forward finite differences.

The `DerivativeCheck` option enables you to
ascertain whether the derivative (gradient) functions that you supply
for objective or constraint functions give *approximately* the
same values as those estimated by a solver using finite differences.
The meaning of "approximately" has changed. Now it means
the relative error of each component of the gradient is less than
10^{–6}, unless the size of an analytically
given component is smaller than 1, in which case it means the absolute
difference is less than 10^{–6}. Previously,
the gradients were considered approximately equal if the maximum absolute
error in any component of the gradient was less than (10^{–6} *
norm of analytic gradient) + 10^{–5}.

Some problems will now report violations of the `DerivativeCheck` condition,
when previously they would not.

`fmincon`, `fminimax`,
and `fgoalattain` can
take finite differences in parallel in order to speed the estimation
of gradients. For details on how to use this parallel gradient estimation,
see the Parallel
Computing for Optimization chapter in the User's Guide.

The Global Optimization Toolbox GUIs `gatool` and `psearchtool` have
been combined into the Optimization Tool GUI. To access these GUIs,
type `optimtool` at the command line, and choose
the appropriate solver.

Furthermore, three new Global Optimization Toolbox solvers
were added to Optimization Tool: `gamultiobj`, `simulannealbnd`,
and `threshacceptbnd`.

Optimization Tool shows Global Optimization Toolbox solvers only if these solvers are licensed.

The new interior-point algorithm is a large-scale algorithm
that can handle all types of constraints. It has several new options,
explained in the `fmincon` function
reference pages.

`fmincon` now
has three algorithms. Choose between them by setting the new option `Algorithm` to:

`'trust-region-reflective'`(formerly known as`'large scale'`)`'active-set'`(formerly known as`'medium scale'`)`'interior-point'`

By default, `Algorithm` = `'trust-region-reflective'`.

The previous way of choosing the algorithm at the command line
was to set option `LargeScale` to `'on'` or `'off'`. `LargeScale` is
now ignored, except when `LargeScale` = `'off'` and `Algorithm` = `'trust-region-reflective'`.
In this case, the `'active-set'` algorithm is used,
to minimize backward incompatibility.

Use the new `ktrlink` function
to call KNITRO optimization
libraries from Ziena Optimization, Inc. KNITRO libraries must be purchased
separately. The External Interface chapter
of the User's Guide describes the `ktrlink` function.

The default value of the `PrecondBandWidth` option
changed from 0 to `Inf` for the `lsqcurvefit`, `lsqnonlin`,
and `fsolve` solvers.
This change was beneficial in the vast majority of tested problems.

In Optimization Tool, the default in **Algorithm settings
> Subproblem algorithm** is now **Cholesky
factorization**, instead of **Preconditioned
CG** = 0.

The new default can lead to slower performance for problems with high-dimensional nonlinearities. If this happens, change the default to another value such as 0 (the previous default).

The new `TolConSQP` option exposes a parameter
that was fixed at `eps` before. The parameter is
used in the `fmincon`, `fminimax`, `fgoalattain`,
and `fseminf` solvers.

The new default value is `TolConSQP` = 1e–6.
This did not affect a vast majority of tested cases, and was beneficial
in some. If you want exactly the same behavior as before, set `TolConSQP` = `eps` using `optimset`.

The `constrviolation` field now exists in the
output structure for the `fgoalattain`, `fmincon`, `fminimax`,
and `fseminf` functions;
it measures the nonlinear constraint violation.

`fminimax`now returns the value of`max(fval)`in the output`maxfval`.The iterative display of

`fminimax`and`fgoalattain`have changed.

The third output argument of the solver

`fminimax`,`maxfval`, is described in the documentation as the maximum of the objective functions in the input`fun`evaluated at the solution`x`, that is,`max(fval)`. Before this release,`fminimax`actually returned the maximum of the objective functions in the reformulated minimax problem internally constructed by the algorithm. This value was typically very close to, but not necessarily equal to,`max(fval)`.`fminimax`now returns the exact value of`max(fval)`in the output`maxfval`.The iterative display for

`fminimax`includes a new column with header`Objective value`that reports the objective function value of the nonlinear programming reformulation of the minimax problem. The column header`Max{F,constraints}`has been changed to`Max constraint`, and the column now contains the maximum violation among all constraints, both internally constructed and user-provided.The iterative display for

`fgoalattain`now shows the value of the attainment factor in the`Attainment factor`column. A new column,`Max constraint`, contains the maximum violation among all constraints, both internally constructed and user-provided.

The Optimization Tool is a graphical user interface (GUI) for
performing common optimization tasks with the Optimization Toolbox.
Using the `optimtool`,
you can do the following:

Select a solver and define your optimization problem.

Set and inspect optimization options and their default values.

Run problems and visualize results.

Import and export problem definitions, algorithm options, and results between the MATLAB workspace and the Optimization Tool.

Automatically generate M-code to capture, automate, and recreate your problem.

Access built-in help.

You can now specify the `PlotFcns` option in
the `optimset` function or using the Optimization
Tool for use with an Optimization Toolbox solver. With this option,
you can plot various measures of progress while the algorithm executes.
You can select from several predefined plots, or you can write your
own.

You can now specify more than one output function in the `OutputFcn` option.

The output function input `x` and fields in
the `optimValues` structure have the following changes
that address bugs in previous releases:

`residual`now returns the residual vector for`lsqnonlin`and`lsqcurvefit`.`resnorm`contains the sum of squares and has been added for`lsqnonlin`and`lsqcurvefit`. The previous field`fval`has been removed for these functions.`procedure`has been removed for`lsqnonlin`,`lsqcurvefit`, and`fsolve`.`x`now returns the expected shape and size for`fgoalattain`and`fminimax`.

The above changes to the input `x` and `optimValues` structure
have the following compatibility considerations in the output function:

If you have references to the

`residual`in a previous version, note that the value of this field has changed for`lsqnonlin`and`lsqcurvefit`. This fixes the problem addressed by the bug report S-289285.Any references to

`fval`for`lsqnonlin`and`lsqcurvefit`need to be updated to`resnorm`. This fixes the problem addressed by the bug report S-289285.Any references to

`procedure`for`lsqnonlin`and`lsqcurvefit`need to be removed. This fixes the problem addressed by the bug report S-291974.Previously, for

`fgoalattain`and`fminimax`,`x`returned a column vector with an additional last element. If you have references to the values for`x`in a previous version, the extra element must be removed and the output vector may need to be reshaped. This fixes the problem addressed by the bug report S-315658.

You can now set the optimization option `Display` to `'notify'` for
the functions `fmincon`, `fminunc`, `fminimax`, `fgoalattain`,
and `fseminf`.
When `Display` is set to `'notify'`,
the output is displayed only if the function does not converge.

Release | Features or Changes with Compatibility Considerations |
---|---|

R2014a | |

R2013b | Solvers that check initial point more carefully |

R2013a | |

R2012b | Changing default algorithms for fmincon and quadprog |

R2012a | |

R2011b | |

R2011a | |

R2010b | None |

R2010a | lsqnonneg No Longer Uses x0 |

R2009b | |

R2009a | |

R2008b | |

R2008a | |

R2007b | None |

R2007a | Changes to Outputs of Multiobjective Solvers |

R2006b | Changes to the Output Function |

R2006a | None |

R14SP3 | None |

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© 1994-2014 The MathWorks, Inc.